The model is publicly available on the GitHub repository.
LEAP Concept Map
LEAP was developed with key factors that impact childhood asthma in mind. We identified risk factors through a formal conceptualization process involving respirologists, allergists, and modelers1. They can be categorized as (1) family history, (2) patient characteristics, and (3) environmental factors. An arrow indicates the direction of the relationship, along with the strength written above it.
Model Implementation
After an initial implementation in Julia, the LEAP model was ported to Python guided by three key principles: clarity, consistency, and correctness. Clarity is achieved by documenting every function, class, table, and algorithm with clear “docstrings” that describe parameters, logic, and purpose in plain English. Consistency comes from applying uniform styling across the codebase, docstrings, and comments. Correctness is ensured through a review pipeline that combines manual code review with an automated test suite that blocks failing code from being integrated. Together, these principles make Python an ideal choice for LEAP, supporting transparency, reproducibility, and long-term maintainability. This foundation ensures LEAP is well-prepared to answer health policy questions while remaining adaptable to future development. A Jupyter Notebook with sample results is available here: https://resplab.github.io/leap/cli/validation.html
LEAP Model Components
The asthma model consists of five intertwined modules: 1) demographics, 2) risk factors, 3) asthma occurrence, 4) asthma outcomes, and 5) payoffs (costs and utilities).
Demographics
The demographics module consists of birth, immigration, emigration and mortality equations. At the start of the simulation, an initial population is generated for the specified base year. In subsequent years, virtual individuals enter the simulated population through birth or immigration according to the estimates or projections of population growth and aging, and exit the simulated population when one of the following events occur: death, emigration, or reaching the end of the time horizon.
Risk Factors
In the current version of the model, we model the age, biological sex, family history of asthma at birth, and infant (< 1 year of age) antibiotic use of all individuals, which predict the probability of future events in the model such as the development of asthma or occurrence of exacerbations.
Asthma Occurrence
All simulated individuals 3 years of age and older are assigned an asthma status based on their risk factors at baseline. Individuals who do not have asthma at baseline are at risk of developing asthma throughout the model time horizon. We do not model asthma in children under 3 years of age, as it is difficult to perform tests to perform and confirm asthma assessment for that age group (Jones et al., 2019).
Asthma Outcomes
Two main features of the disease course of asthma are (1) asthma control and (2) asthma exacerbation.
- Asthma control refers to how well asthma and the risk of adverse outcomes can be managed with risk factor modifications or treatment, and is defined as uncontrolled, partially controlled, or well controlled. For each individual, we model time spent in each control category, which partly determines an individual’s economic costs and health-related quality of life.
- An asthma exacerbation (or a ‘flare-up’) is a sudden worsening of asthma symptoms, which often results in healthcare use, including hospitalizations. We model the number and severity of exacerbations for each individual determined by their sex, age, and past history of exacerbations.
Payoffs
The payoffs module is responsible for assigning utilities and costs. The utility values measure health-related quality based on individual’s asthma control level and the number and severity of exacerbations they experience. LEAP also models the health-related costs of treatment and management of asthma (by control level) and treatment of asthma exacerbations.
Schematic illustration of the reference asthma policy model
A Living Reference Model
LEAP is a reference platform supporting multiple health policy modeling projects. Each project is independently developed and validated, with tailored strategies for patient and public engagement. The platform is managed by Principal Investigator Dr. Kate Johnson, with guidance from multiple groups.
LEAP’s governance includes ongoing review cycles, transparent documentation of model changes, and stakeholder engagement embedded within each project. This structure ensures LEAP remains a credible, flexible, and responsive tool for health policy research.
Each LEAP project is developed and validated independently. This ensures engagement strategies are tailored to patient and public partners, validation is context-specific using both quantitative and qualitative methods, and model assumptions and updates are transparently documented.